Consuming patients’ days: Time spent on ambulatory visits by persons with cancer.
Bibliographic record
Abstract
324 Background: Health care-related time burdens faced by persons with cancer can be substantial. In qualitative work, patients report that ‘’seemingly short clinic visits (e.g., a supposed 10-minute blood draw) often turn into all-day affairs.” We sought to estimate the total time spent (home-to-home) by patients with cancer on ambulatory services at an urban cancer center. Methods: We conducted a retrospective study of all patients with cancer scheduled for any ambulatory care (e.g., laboratory testing, imaging, procedures, infusions, clinician visits) at a Midwestern academic cancer center during a randomly selected week (Monday-Friday) in January 2023. We extracted sociodemographics, clinical characteristics, and services received from the electronic medical record. The primary exposure was the ambulatory care type or combination received by a patient on a day (e.g., clinician visit only, labs and infusion, etc.). Using data from the Real-Time Location System (RTLS) badge that patients wear from clinic entry to exit (standard of care to optimize clinic workflow), we tracked time spent in each patient location (e.g., exam room) and in the clinic overall. We estimated round-trip travel times using home and clinic zip codes and SAS/Google Maps. We calculated median parking time by directly observing 20 random patients between the clinic and their vehicles (e.g., parking ramp or valet). We calculated and summarized clinic and total (clinic + travel + parking) times for different ambulatory care types. Results: We analyzed 468 encounters by 436 unique patients (median age, 64 years, 52% women, 82% white race, 19% breast cancer). Median (IQR) clinic time per encounter was 118 (76-200) minutes. The median (IQR) round-trip driving distance and travel time was 33 (21-53) miles and 50 (36-68) minutes. The median parking time was 8 (6-10) minutes. The median (IQR) total time was 189 (136-277) minutes. Table 1 presents the times by type(s) of ambulatory services. Conclusions: In this single-center retrospective study, pursuing ambulatory cancer care on a given day required patients (and their accompanying care partners) to commit several hours to their care. These findings highlight the need to decrease care time demands on patients to mitigate time toxicity. Accounting for opportunity time costs and the coordination of other daily activities around ambulatory care, these results support our previously proposed practical measure of the time burdens of cancer care—that any day with in-person healthcare contact may represent a ‘’lost day.”[Table: see text]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".